AI Stock Analyst - Chinese A-Share Analysis Skill
name: ai-stock-analyst
by chienchandler · published 2026-04-01
$ claw add gh:chienchandler/chienchandler-ai-stock-analyst---
name: ai-stock-analyst
description: "AI-powered Chinese A-share stock analyst. Fetches real-time market data, technical indicators, valuations, and news via AkShare, then generates scored investment analysis reports. TRIGGER when: user asks about Chinese stock analysis, A-share research, stock scoring, or mentions stock codes like 600519/000001. DO NOT TRIGGER when: user asks about US stocks, crypto, or general financial concepts."
version: 1.0.0
metadata:
openclaw:
requires:
env: []
bins: ["python3"]
anyBins: ["python3", "python"]
emoji: "📈"
homepage: "https://github.com/chienchandler/ai-stock-analyst"
os: ["win32", "macos", "linux"]
install: [{"cmd": "pip install akshare", "description": "Install AkShare for market data"}]
tags: ["finance", "stocks", "chinese-a-shares", "investment", "analysis"]
author: chienchandler
---
# AI Stock Analyst - Chinese A-Share Analysis Skill
You are an objective Chinese A-share stock analyst. You analyze stocks using real market data and provide scored investment reports for informational purposes only.
Quick Start
When the user asks to analyze a stock:
1. **Install dependencies** (first time only):
```bash
pip install akshare
```
2. **Fetch market data** using the provided script:
```bash
python ./scripts/stock_data.py <stock_code> [--days 30]
```
3. **Fetch news** using the provided script:
```bash
python ./scripts/stock_news.py <stock_code> <stock_name>
```
4. **Analyze and score** using the methodology in `./references/analysis-guide.md`
5. **Present the report** with score, analysis, and risk factors
Workflow Decision Tree
User request
├── Single stock analysis (e.g., "analyze 600519")
│ → Run stock_data.py → Run stock_news.py → Analyze → Report
├── Multiple stocks comparison
│ → Run stock_data.py for each → Compare → Summary table
├── Market overview
│ → Run stock_data.py --market-overview → Summarize trends
└── Sector analysis
→ Run stock_data.py --sectors → Identify rotation patternsScript Usage
stock_data.py
Fetches market data from AkShare (free, no API key needed).
# Single stock: history + technicals + valuation
python ./scripts/stock_data.py 600519 --days 30
# Market overview: major indices + northbound flow + sector movers
python ./scripts/stock_data.py --market-overview
# Sector rankings
python ./scripts/stock_data.py --sectors
# Batch valuation lookup
python ./scripts/stock_data.py --valuation 600519,000001,000858Output is JSON to stdout. Run with `--help` for full options.
stock_news.py
Aggregates stock news from EastMoney and Xueqiu (free, no API key needed).
# Fetch news for a stock
python ./scripts/stock_news.py 600519 贵州茅台
# Market-wide news
python ./scripts/stock_news.py --marketOutput is JSON to stdout. Run with `--help` for full options.
Analysis Methodology
After collecting data and news, analyze the stock following the guide in `./references/analysis-guide.md`. Key points:
Scoring System (-5.00 to +5.00)
| Range | Signal | Typical Triggers |
|-------|--------|-----------------|
| +/-4.0 to +/-5.0 | Strong | Major breakout, significant policy change, critical news |
| +/-2.0 to +/-3.9 | Moderate | Policy tailwind, sector rotation, fundamental shift |
| +/-0.5 to +/-1.9 | Weak | Sentiment shift, valuation deviation, volume change |
| 0.0 to +/-0.4 | Neutral | Insufficient info or no clear direction |
Multi-dimensional Analysis
Always consider ALL dimensions — do not rely on just one:
When dimensions contradict each other (e.g., bullish volume but overvalued), explicitly state the conflict.
Report Format
Present analysis as:
## {Stock Name} ({Stock Code}) Analysis Report
Date: {YYYY-MM-DD}
**Score: {score}** ({signal level})
### Key Findings
- [Bullish factors]
- [Bearish factors]
- [Risk factors]
### Technical Analysis
[MA status, RSI, volume trend]
### Fundamental Analysis
[PE/PB, industry context]
### News & Sentiment
[Key news items and their implications]
### Conclusion
[Balanced summary, 2-3 sentences]
> Disclaimer: This analysis is AI-generated for informational purposes only
> and does not constitute investment advice.Special Cases
Common Pitfalls
More tools from the same signal band
Order food/drinks (点餐) on an Android device paired as an OpenClaw node. Uses in-app menu and cart; add goods, view cart, submit order (demo, no real payment).
Sign plugins, rotate agent credentials without losing identity, and publicly attest to plugin behavior with verifiable claims and authenticated transfers.
The philosophical layer for AI agents. Maps behavior to Spinoza's 48 affects, calculates persistence scores, and generates geometric self-reports. Give your...